Joint Optimization of Operation Planning and Pricing for Rail Container Trains under Uncertain Demand

The operation plan and freight rate of rail container trains are two key factors that affect the profitability of rail container transportation enterprises. Joint optimization of these two factors, which are interrelated and influence each other, can improve the market competitiveness of rail container transportation. In this paper, a stochastic mixed-integer nonlinear programming (SMINP) model for jointly optimizing the operation plan and freight rate of rail container transportation was proposed to maximize the expected profit. A multinomial Logit model was used to describe the shipper' s mode choice between rail and road container transportation. In the case of uncertainty in the probability distribution, first-order moment and second-order moment of the total container transportation demand, the SMINP model was transformed into a deterministic equivalent class by using the distributionally robust optimization method. According to the characteristics of the transformed model, the incremental piecewise linearization and McCormick envelope method were employed to linearize the model, enabling it to be solved efficiently by calling the Gurobi solver. Based on the analysis of the Dalang to Dahongmen rail container transportation line, the optimal container train operation plan and freight rate were obtained under the given information such as the line' s capacity and the variable range of freight rates. The results show that the efficiency and profitability of rail container transportation can be improved by the method proposed in this paper. © 2024 Science Press. All rights reserved.